Research

__(names underlined are students and postdocs)

Publications

1. Chekouo, T., & Mukherjee, H. (2024). A Bayesian Hierarchical Hidden Markov Model for clustering and gene selection: Application to Kidney cancer gene expression data. Biometrical Journal, 66. https://doi.org/10.1002/bimj.202300173

2. Fatemi, H. S., Eschenlauer, A., Aman, J., Folsom, T., & Chekouo, T. (2024). Quantitative Proteomics of Dorsolateral Prefrontal Cortex Reveals an Early Pattern of Synaptic Dysmaturation in Children with Idiopathic Autism. Cerebral Cortex, 34(13), 161-171. PubMed Central ID Number: PMID: 38696595 doi: doi: 10.1093/cercor/bhae044

3.  Dai, X., Acosta, N., Lu, X., Hubert CRJ, Lee, J., Frankowski, K., . . . Chekouo, T. (2024). A Bayesian framework for modeling COVID-19 case numbers through longitudinal monitoring of SARS-CoV-2 RNA in wastewater. Statistics in medicine, 43(6), 1153-1169.  doi: 10.1002/sim.10009

4. Chekouo, T., Stingo, F. C., Mohammed, S., Rao, A., & Baladandayuthapani, V. (2023). A Bayesian group selection with compositional responses for analysis of radiologic tumor proportions and their genomic determinants. The Annals of Applied Statistics, 17(4), 3013--3034. 

5. Chekouo, T., & Safo^, S. E. (2023). Bayesian integrative analysis and prediction with application to atherosclerosis cardiovascular disease. Biostatistics, 24(1), 124--139. 

6. Wang, M., Sajobi, T., Ganesh, A., Seitz, D., Chekouo, T., Forkert, N. D., Borrie, M., Camicioli, R., Hsiung, G., Masellis, M., Moorhouse, P., Hogan, D., Trataglia, M.C., Ismail, Z., & Smith E.E. (2023). Identifying prognostic factors of dementia in individuals with mild cognitive impairment (MCI): Are statistical models adequate? Alzheimer's & Dementia, 19, e064702. doi.org/10.1002/alz.064702

7.  He, J., Antonyan, L., Zhu, H., Ardila, K., Li, Q., Enoma, D., Chekouo, T., . . . Long, Q. (2023). A statistical method for image-mediated association studies discovers genes and pathways associated with four brain disorders. American journal of human genetics.  doi: 10.1016/j.ajhg.2023.11.006

8. Acosta, N., Dai, X., Bautista, M. A., Waddell, B. J., Lee, J., Du, K., . . ., Chekouo, T., …, Parkins, M. D. (2023). Wastewater-based surveillance can be used to model COVID-19-associated workforce absenteeism. The Science of the total environment, 900, 165172. PubMed Central ID Number: PMC10292917 doi: 10.1016/j.scitotenv.2023.165172

9. Lee, J., Acosta, N., Waddell, B. J., Du, K., Xiang, K., Van Doorn, J., . . . ., Chekouo, T., …, Parkins, M. D. (2023). Campus node-based wastewater surveillance enables COVID-19 case localization and confirms lower SARS-CoV-2 burden relative to the surrounding community. Water research, 244, 120469.  doi: 10.1016/j.watres.2023.120469

10. Wang, M., Sajobi, T. T., Hogan, D. B., Ganesh, A., Seitz, D. P., Chekouo, T., . . . Smith, E. E. (2023). Expert elicitation of risk factors for progression to dementia in individuals with mild cognitive impairment. Alzheimer's & dementia : the journal of the Alzheimer's Association, 19(10), 4542-4548.  doi: 10.1002/alz.12987

11. Dai, X., Lu, X., & Chekouo, T. (2023). A Bayesian genomic selection approach incorporating prior feature ordering and population structures with application to coronary artery disease. Statistical methods in medical research, 32(8), 1616-1629.  doi: 10.1177/09622802231181231

12. Biziaev, T., Aktary, M. L., Wang, Q., Chekouo, T., Bhatti, P., Shack, L., . . . Kopciuk, K. A. (2023). Development and External Validation of Partial Proportional Odds Risk Prediction Models for Cancer Stage at Diagnosis among Males and Females in Canada. Cancers, 15(14). PubMed Central ID Number: PMC10377619 doi: 10.3390/cancers15143545

13. Lipman, D., Safo, S. E., & Chekouo, T. (2023). Integrative multi-omics approach for identifying molecular signatures and pathways and deriving and validating molecular scores for COVID-19 severity and status. BMC genomics, 24(1), 319. PubMed Central ID Number: PMC10259816 doi: 10.1186/s12864-023-09410-5

14. Wang, M., Chekouo, T., Ismail, Z., Forkert, N. D., Hogan, D. B., Ganesh, A., . . . Sajobi, T. T. (2023). Elicited clinician knowledge did not improve dementia risk prediction in individuals with mild cognitive impairment. Journal of clinical epidemiology, 158, 111-118.  doi: 10.1016/j.jclinepi.2023.03.009

15.  Lu, X., Chekouo, T., Shen, H., & de Leon, A. R. (2023). A two-level copula joint model for joint analysis of longitudinal and competing risks data. Statistics in medicine, 42(12), 1909-1930.  doi: 10.1002/sim.9704

16. Acosta, N., Bautista, M. A., Waddell, B. J., Du, K., McCalder, J., Pradhan, P., . . . Parkins, M. D. (2023). Surveillance for SARS-CoV-2 and its variants in wastewater of tertiary care hospitals correlates with increasing case burden and outbreaks. Journal of medical virology, 95(2), e28442. PubMed Central ID Number: PMC9880705 doi: 10.1002/jmv.28442

17. Wang, M., Sajobi, T. T., Ismail, Z., Seitz, D., Chekouo, T., Forkert, N. D., . . . Smith, E. E. (2022). A pragmatic dementia risk score for patients with mild cognitive impairment in a memory clinic population: Development and validation of a dementia risk score using routinely collected data. Alzheimers & Dementia-Translational Research & Clinical Interventions, 8(1).  doi: 10.1002/trc2.12301

18. Li, W., & Chekouo, T. (2022). Bayesian group selection with non-local priors. Computational Statistics, 37(1), 287-302.  doi: 10.1007/s00180-021-01115-1

19. Lipman, D., Safo, S. E., & Chekouo, T. (2022). Multi-omic analysis reveals enriched pathways associated with COVID-19 and COVID-19 severity. PLOS ONE, 17(4).  doi: 10.1371/journal.pone.0267047

20. Dai, X., Champredon, D., Fazil, A., Mangat, C. S., Peterson, S. W., Mejia, E. M., . . . Chekouo, T. (2022). Statistical framework to support the epidemiological interpretation of SARS-CoV-2 concentration in municipal wastewater. Scientific Reports, 12(1).  doi: 10.1038/s41598-022-17543-y

21. Wang, M., Greenberg, M., Forkert, N. D., Chekouo, T., Afriyie, G., Ismail, Z., . . . Sajobi, T. T. (2022). Dementia risk prediction in individuals with mild cognitive impairment: a comparison of Cox regression and machine learning models. BMC Medical Research Methodology, 22(1).  doi: 10.1186/s12874-022-01754-y

22. Acosta, N., Bautista, M. A., Waddell, B. J., McCalder, J., Beaudet, A. B., Man, L., . . . , Chekouo, T., …,Parkins, M. D. (2022). Longitudinal SARS-CoV-2 RNA wastewater monitoring across a range of scales correlates with total and regional COVID-19 burden in a well-defined urban population. Water Research, 220.  doi: 10.1016/j.watres.2022.118611

23. Chen, X., Zhang, Q., & Chekouo, T. (2021). Filtering High-Dimensional Methylation Marks With Extremely Small Sample Size: An Application to Gastric Cancer Data. Frontiers in genetics; computational biology, 12.  doi: 10.3389/fgene.2021.705708

24. Wang, M., Smith, E. E., Forkert, N. D., Chekouo, T., Ismail, Z., Ganesh, A., & Sajobi, T. (2021). Integrating expert knowledge for dementia risk prediction in individuals with mild cognitive impairment (MCI): a study protocol. BMJ Open, 11(11).  doi: 10.1136/bmjopen-2021-051185

25. Chekouo, T., Mohammed, S., & Rao, A. (2020). A Bayesian 2D functional linear model for gray-level co-occurrence matrices in texture analysis of lower grade gliomas. Neuroimage Clinical, 28.  doi: 10.1016/j.nicl.2020.102437

26. Chekouo, T., Stingo, F. C., Class, C. A., Yan, Y., Bohannan, Z., Wei, Y., . . . Do, K.-A. (2020). Investigating protein patterns in human leukemia cell line experiments: A Bayesian approach for extremely small sample sizes. Statistical Methods in Medical Research, 29(4), 1181-1196.  doi: 10.1177/0962280219852721

27. Jeffords, E., Freeman, S., Cole, B., Root, K., Chekouo, T., Melvin, R. G., . . . Simmons, Glenn E., Jr. (2020). Y-box binding protein 1 acts as a negative regulator of stearoyl CoA desaturase 1 in clear cell renal cell carcinoma. Oncology Letters, 20(5).  doi: 10.3892/ol.2020.12026

28.  Chekouo, T., & Murua, A. (2018). High-dimensional Variable Selection with the Plaid Mixture Model for Clustering. Computational Statistics, 33(3), 1475-1496. 

29. Chekouo, T., Stingo, F. C., Doecke, J. D., & Do, K.-A. (2017). A Bayesian integrative approach for multi-platform genomic data: A kidney cancer case study. Biometrics, 73(2), 615-624. 

30.  Chekouo, T., Stingo, F. C., Guindani, M., & Do, K.-A. (2016). A Bayesian predictive model for imaging genetics with application to schizophrenia. The Annals of Applied Statistics, 10(3), 1547–1571. 

31. Chekouo, T., Stingo, F. C., Doecke, J. D., & Do, K.-A. (2015). miRNA–target gene regulatory networks: A Bayesian integrative approach to biomarker selection with application to kidney cancer. Biometrics, 71(2), 428–438. 

32. Chekouo, T., Murua, A., & Raffelsberger, W. (2015). The Gibbs-plaid biclustering model. The Annals of Applied Statistics, 9(3), 1643–1670. 

33. Chekouo, T., & Murua, A. (2015). The penalized biclustering model and related algorithms. Journal of Applied Statistics, 42(6), 1255–1277. 

34. Doecke, J. D., Chekouo, T. T., Stingo, F., & Do, K.-A. (2014). miRNA Target Gene Identification: Sourcing miRNA Target Gene Relationships for the Analyses of TCGA Illumina MiSeq and RNA-Seq Hiseq Platform Data. Int J Hum Genet, 14(1), 17–22. 

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